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Most of the new solutions found by our algorithm have lower complexity (in terms of number of transitions rules used).

The pathways identified by our algorithm have previously been confirmed in the literature as viable, high-yield synthesis routes.

Looking at the left side of   Figure 2, one can notice that top ranked genes for the AML datasets (i.e., high number of co-citations between the gene and the disease) have, in general, a lower rank for the other two disease (i.e., lower number of co-citations), thus giving an indication that the genes selected by our algorithm have a higher bibliometric correlation with the selected disease.

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The majority of the verified TSSs recalled by our algorithm had a dominant coverage on the 5' end of the transcript, one of such cases is shown in Additional file 1: Figure S7.

The cooling channels designed by our algorithms has very simple connectivity and can achieve effective conformal cooling for the models with complex shapes.

Copies of our algorithm have already been requested by several ICUs in the UK.

Our results show that, if all features are used or the features selected by t-test, our algorithm has much lower accuracy.

Our colocalization algorithm was designed to contemplate this variability and was precise and consistent in a variety of simulated images including those where colocalization cannot be determined by eye (i.e., our algorithm has sub-visual capacity), and in the biological samples evaluated here as well as in other models.

Verified with the reference by the experienced neurologist (Chen), our algorithm had high sensitivity, specificity, and SI.

The second algorithm was the upper bounds of the lifetime protocol, and our algorithm has improved lifetime by 73.00 and 80.00% for WSNs optimization of network lifetime algorithms.

A comprehensive comparison among the existing algorithms and our algorithm has been made by comparing the predicted complexes against benchmark complexes.

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